Structural partitioning of complex structures in the medium-frequency range. An application to an automotive vehicle

Kassem, Morad; Soize, Christian; Gagliardini, Laurent

doi:10.1016/j.jsv.2010.09.008

Cited by 32 publications

(18 citation statements)

References 23 publications

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“…is the Hermitian norm, where U(ω) is the reference solution calculated with Eq. (1) and where U mod;a (ω) is the solution of the following modified G-ROM, of uncertainties has been identified and validated for automotive vehicle in [11,12,13]. In particular, it has been proven that the confidence regions of the random responses are not really sensitive to the damping uncertainties.…”

Section: Global Reduced Order Modelmentioning

confidence: 99%

Stochastic reduced order computational model of structures having numerous local elastic modes in low frequency dynamics

Arnoux¹,

Batou²,

Soize³

et al. 2013

Journal of Sound and Vibration

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This paper is devoted to the construction of a stochastic reduced order computational model of structures having numerous local elastic modes in low frequency dynamics. We are particularly interested in automotive vehicles which are made up of stiff parts and flexible components. This type of structure is characterized by the fact that it exhibits, in the low frequency range, not only the classical global elastic modes but also numerous local elastic modes which cannot easily be separated from the global elastic modes. To solve this difficult problem, an innovative method has recently been proposed for constructing a reduced order computational dynamical model adapted to this particular situation for the low frequency range. Then a new adapted generalized eigenvalue problem is introduced and allows a global vector basis to be constructed for the global displacements space. This method requires to decompose the domain of the structure into sub-domains. Such a decomposition is carried out using the Fast Marching Method.

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Section: Global Reduced Order Modelmentioning

confidence: 99%

Stochastic reduced order computational model of structures having numerous local elastic modes in low frequency dynamics

Arnoux¹,

Batou²,

Soize³

et al. 2013

Journal of Sound and Vibration

Self Cite

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“…This prior probability distribution is constructed by using the Maximum Entropy Principle [20] (from Information Theory [21]) for which the constraints are defined by the available information [13,14,22,15]. Since the paper [13], many works have been published in order to validate the nonparametric probabilistic approach of model uncertainties with experimental results (see for instance [23,24,25,26,27,28,15,29]), to extend the applicability of the theory to other areas [30,31,32,33,34,35,36,37,38,39,40,41], to extend the theory to new ensembles of positive-definite random matrices yielding a more flexible description of the dispersion levels [42], to apply the theory for the analysis of complex dynamical systems in the medium-frequency range, including vibroacoustic systems, [43,44,23,45,25,26,27,28,46,47,48,39], to analyze nonlinear dynamical systems (i) for local nonlinear elements [49,50,37,…”

Section: Types Of Approach For Stochastic Modeling Of Uncertaintiesmentioning

confidence: 99%

Stochastic modeling of uncertainties in computational structural dynamics—Recent theoretical advances

Soize

2013

Journal of Sound and Vibration

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140

106

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To cite this version:Christian Soize. Stochastic modeling of uncertainties in computational structural dynamics -Recent theoretical advances. Journal of Sound and Vibration, Elsevier, 2013, 332 (10) AbstractThis paper deals with a short overview on stochastic modeling of uncertainties. We introduce the types of uncertainties, the variability of real systems, the types of probabilistic approaches, the representations for the stochastic models of uncertainties, the construction of the stochastic models using the maximum entropy principle, the propagation of uncertainties, the methods to solve the stochastic dynamical equations, the identification of the prior and the posterior stochastic models, the robust updating of the computational models and the robust design with uncertain computational models. We present recent theoretical advances in this field concerning the parametric and the nonparametric probabilistic approaches of uncertainties in computational structural dynamics for the construction of the prior stochastic models of both the uncertainties on the computational model parameters and on the modeling uncertainties, and for their identification with experimental data. We also present the construction of the posterior stochastic model of uncertainties using the Bayesian method when experimental data are available.

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“…The attribute parameters of subsystems can also be calculated accurately to achieve precise dynamic prediction results. However, substructures in practical applications often have complex geometrical and physical properties, and no related attribute parameter is available for reference [4]. Moreover, experiments for testing the attribute parameters of substructures are often time consuming and are thus not widely used in engineering practice.…”

Section: Introductionmentioning

confidence: 99%

SEA Subsystem Attribute Correction Based on Stiffness Multipliers

Gao¹,

Yong²

2016

Proceedings of the 2016 International Conference on Electrical, Mechanical and Industrial Engineering

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Abstract-This study introduces the principles and application procedures of the stiffness multiplier method used to correct the statistical energy analysis (SEA) subsystem attributes. Using the cockpit floor of a certain type of fighter aircraft as an example, this study combines the finite element method and stiffness multiplier correction technique to modify the modal density of the SEA model through theoretical derivation and numerical simulation analysis. Results of the numerical simulation show that the SEA subsystem modified with the stiffness multiplier method can perfectly reflect the dynamic characteristics of complex stiffened plates. Moreover, the results of the calculation are identical to those obtained with the finite element method. The stiffness multiplier method is found to be applicable to the construction of the complex structures of the SEA subsystem model.

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Structural partitioning of complex structures in the medium-frequency range. An application to an automotive vehicle

Cited by 32 publications

References 23 publications

Stochastic reduced order computational model of structures having numerous local elastic modes in low frequency dynamics

Stochastic reduced order computational model of structures having numerous local elastic modes in low frequency dynamics

Stochastic modeling of uncertainties in computational structural dynamics—Recent theoretical advances

SEA Subsystem Attribute Correction Based on Stiffness Multipliers

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